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2018 | OriginalPaper | Buchkapitel

Performance Evaluation of Selected Thermal Imaging-Based Human Face Detectors

verfasst von : Paweł Forczmański

Erschienen in: Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017

Verlag: Springer International Publishing

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Abstract

The paper is devoted to the problem of face detection in thermal imagery. Its aim was to investigate several contemporary general-purpose object detectors known to be accurate when working in visible lighting conditions. Employed classifiers are based on AdaBoost learning method with three types of low-level descriptors, namely Haar–like features, Histogram of Oriented Gradients, and Local Binary Patterns. Additionally, the performance of recently proposed Max-Margin Object-Detection Algorithm joint with HOG feature extractor and Deep Neural Network-based approach have been investigated. Performed experiments, on images taken in controlled and uncontrolled conditions, gathered in our own benchmark database and in a few other databases support final observations and conclusions.

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Vorheriges Kapitel Efficient Sketch Recognition Based on Shape Features and Multidimensional Indexing

Nächstes Kapitel On a New Method of Dynamic Integration of Fuzzy Linear Regression Models

Burduk, R.: The AdaBoost algorithm with the imprecision determine the weights of the observations. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) ACIIDS 2014, Part II. LNCS (LNAI), vol. 8398, pp. 110–116. Springer, Cham (2014). doi:10.1007/978-3-319-05458-2_12 CrossRef

Chang, H., Koschan, A., Abidi, M., Kong, S.G., Won, C.-H.: Multispectral visible and infrared imaging for face recognition. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–6 (2008)

Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)

Dowdall, J., Pavlidis, I., Bebis, G.: Face detection in the near-ir spectrum. Image Vis. Comput. 21(7), 565–578 (2001)CrossRef

Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98–136 (2015)CrossRef

Forczmański, P., Kukharev, G.: Comparative analysis of simple facial features extractors. J. Real-Time Image Process. 1(4), 239–255 (2007)CrossRef

Forczmański, P., Kukharev, G., Shchegoleva, N.: Simple and robust facial portraits recognition under variable lighting conditions based on two-dimensional orthogonal transformations. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8156, pp. 602–611. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41181-6_61 CrossRef

Forczmański, P.: Human face detection in thermal images using an ensemble of cascading classifiers. In: Kobayashi, S., Piegat, A., Pejaś, J., El Fray, I., Kacprzyk, J. (eds.) Hard and Soft Computing for Artificial Intelligence, Multimedia and Security. AISC, vol. 534, pp. 205–215. Springer, Cham (2017). doi:10.1007/978-3-319-48429-7_19 CrossRef

Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995). doi:10.1007/3-540-59119-2_166 CrossRef

10.

Ghiass, R.S., Arandjelovic, O., Bendada, H., Maldague, X.: Infrared face recognition: a literature review. In: International Joint Conference on Neural Networks (cs.CV) (2013). arXiv:1306.1603

11.

Hermans-Killam, L.: Cool Cosmos/IPAC website, Infrared Processing and Analysis Center. http://coolcosmos.ipac.caltech.edu/image_galleries/ir_portraits.html. Accessed 10 May 2016

12.

Jasiński, P., Forczmański, P.: Combined imaging system for taking facial portraits in visible and thermal spectra. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 7. AISC, vol. 389, pp. 63–71. Springer, Cham (2016). doi:10.1007/978-3-319-23814-2_8 CrossRef

13.

King, D.: Dlib 18.6 released: Make your own object detector! (2015). http://blog.dlib.net/2014/02/dlib-186-released-make-your-own-object.html. Accessed 27 Jan 2017

14.

King, D.E.: Max-Margin Object Detection, Computer Vision and Pattern Recognition (cs.CV) (2015). arXiv:1502.00046

15.

Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1106–1114 (2012)

16.

Miezianko, R.: IEEE OTCBVS WS Series Bench – Terravic Research Infrared Database. http://vcipl-okstate.org/pbvs/bench/. Accessed 20 May 2016

17.

Ojala, T., Pietikinen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of the 12th International Conference on Pattern Recognition, vol. 1, pp. 582–585 (1994)

18.

Prokoski, F.J., Riedel, R.: Infrared Identification of Faces and Body Parts. In: BIOMETRICS: Personal Identification in Networked Society. Kluwer (1998)

19.

Smiatacz, M.: Liveness measurements using optical flow for biometric person authentication. Metrol. Meas. Syst. 19(2), 257–268 (2012)CrossRef

20.

Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRef

21.

Wong, W.K., Hui, J.H., Lama, J.A.K., Bin Md Desa, J., Izzati, N., Ishak, N.B., Bin Sulaiman, A., Nor, Y.B.M.: Face detection in thermal imaging using head curve geometry. In: 5th International Congress on Image and Signal Processing (CISP 2012), pp. 1038–1041 (2012)

Titel: Performance Evaluation of Selected Thermal Imaging-Based Human Face Detectors
verfasst von: Paweł Forczmański
Verlag: Springer International Publishing
Buch: Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017
Print ISBN: 978-3-319-59161-2

Electronic ISBN: 978-3-319-59162-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-59162-9_18

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